{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6b3a51b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import camelot as cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1f5a68fe",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " deep_email_recv.py\t\t    inbox.py\t   table_from_pdf.csv\r\n",
      " deep_email_send.py\t\t    __pycache__    ted_talk_downloader.py\r\n",
      "'Extracting Table from PDF.ipynb'   send_mail.py   venv\r\n",
      " hn_news_scraper_no_cred.py\t    send.py\r\n"
     ]
    }
   ],
   "source": [
    "! ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0ba51e3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_pdf=cm.read_pdf(\"https://www.undp.org/content/dam/india/docs/india_factsheet_economic_n_hdi.pdf\", flavor='lattice', pages='1,2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4303182b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<TableList n=4>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bc03e100",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Table shape=(4, 3)>\n",
      "<Table shape=(15, 3)>\n",
      "<Table shape=(14, 4)>\n",
      "<Table shape=(13, 3)>\n"
     ]
    }
   ],
   "source": [
    "for n in input_pdf:\n",
    "    print(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "00e047ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Human Development Indicators</td>\n",
       "      <td></td>\n",
       "      <td>2000</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13</td>\n",
       "      <td>Human Development Index Value (HDI)</td>\n",
       "      <td>0.461</td>\n",
       "      <td>0.547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>HDI Rank (out of 187)</td>\n",
       "      <td></td>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>Inequality Adjusted Human Development Index Value</td>\n",
       "      <td></td>\n",
       "      <td>0.392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "      <td>Loss in HDI due to Inequalities (%)</td>\n",
       "      <td></td>\n",
       "      <td>28.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>2001</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>17</td>\n",
       "      <td>Gender Inequality Index (GII)</td>\n",
       "      <td>0.5531</td>\n",
       "      <td>0.617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>18</td>\n",
       "      <td>GII Rank (out of 146)</td>\n",
       "      <td></td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1996</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>19</td>\n",
       "      <td>Gender Empowerment Measure (GEM)</td>\n",
       "      <td>0.416</td>\n",
       "      <td>0.497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>2001</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>20</td>\n",
       "      <td>Literacy Rate (%)</td>\n",
       "      <td>64.8</td>\n",
       "      <td>74.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>21</td>\n",
       "      <td>Male Literacy Rate (%)</td>\n",
       "      <td>75.3</td>\n",
       "      <td>82.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>22</td>\n",
       "      <td>Female Literacy Rate (%)</td>\n",
       "      <td>53.7</td>\n",
       "      <td>65.46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               0  \\\n",
       "0   Human Development Indicators   \n",
       "1                             13   \n",
       "2                             14   \n",
       "3                             15   \n",
       "4                             16   \n",
       "5                                  \n",
       "6                             17   \n",
       "7                             18   \n",
       "8                                  \n",
       "9                             19   \n",
       "10                                 \n",
       "11                            20   \n",
       "12                            21   \n",
       "13                            22   \n",
       "\n",
       "                                                    1       2      3  \n",
       "0                                                        2000   2011  \n",
       "1                 Human Development Index Value (HDI)   0.461  0.547  \n",
       "2                               HDI Rank (out of 187)            134  \n",
       "3   Inequality Adjusted Human Development Index Value          0.392  \n",
       "4                 Loss in HDI due to Inequalities (%)           28.7  \n",
       "5                                                        2001   2011  \n",
       "6                       Gender Inequality Index (GII)  0.5531  0.617  \n",
       "7                               GII Rank (out of 146)            129  \n",
       "8                                                        1996   2006  \n",
       "9                    Gender Empowerment Measure (GEM)   0.416  0.497  \n",
       "10                                                       2001   2011  \n",
       "11                                  Literacy Rate (%)    64.8  74.04  \n",
       "12                             Male Literacy Rate (%)    75.3  82.14  \n",
       "13                           Female Literacy Rate (%)    53.7  65.46  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_pdf[2].df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2a4423e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = input_pdf[2].df.loc[11:14, 1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "36aa4e30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Literacy Rate (%)</td>\n",
       "      <td>64.8</td>\n",
       "      <td>74.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Male Literacy Rate (%)</td>\n",
       "      <td>75.3</td>\n",
       "      <td>82.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Female Literacy Rate (%)</td>\n",
       "      <td>53.7</td>\n",
       "      <td>65.46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           1     2      3\n",
       "11         Literacy Rate (%)  64.8  74.04\n",
       "12    Male Literacy Rate (%)  75.3  82.14\n",
       "13  Female Literacy Rate (%)  53.7  65.46"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "85c423e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c3e58f5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Literacy Rate (%)</td>\n",
       "      <td>64.8</td>\n",
       "      <td>74.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male Literacy Rate (%)</td>\n",
       "      <td>75.3</td>\n",
       "      <td>82.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Female Literacy Rate (%)</td>\n",
       "      <td>53.7</td>\n",
       "      <td>65.46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          1     2      3\n",
       "0         Literacy Rate (%)  64.8  74.04\n",
       "1    Male Literacy Rate (%)  75.3  82.14\n",
       "2  Female Literacy Rate (%)  53.7  65.46"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "54b29771",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = ['KPI', '2001', '2011']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ff3ae8e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>KPI</th>\n",
       "      <th>2001</th>\n",
       "      <th>2011</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Literacy Rate (%)</td>\n",
       "      <td>64.8</td>\n",
       "      <td>74.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male Literacy Rate (%)</td>\n",
       "      <td>75.3</td>\n",
       "      <td>82.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Female Literacy Rate (%)</td>\n",
       "      <td>53.7</td>\n",
       "      <td>65.46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        KPI  2001   2011\n",
       "0         Literacy Rate (%)  64.8  74.04\n",
       "1    Male Literacy Rate (%)  75.3  82.14\n",
       "2  Female Literacy Rate (%)  53.7  65.46"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "39816d25",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[:,['2001','2011']]=df.loc[:,['2001','2011']].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "67f77cc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"table_from_pdf.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "adb5a62e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " deep_email_recv.py\t\t    inbox.py\t   table_from_pdf.csv\r\n",
      " deep_email_send.py\t\t    __pycache__    ted_talk_downloader.py\r\n",
      "'Extracting Table from PDF.ipynb'   send_mail.py   venv\r\n",
      " hn_news_scraper_no_cred.py\t    send.py\r\n"
     ]
    }
   ],
   "source": [
    "! ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "596747d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_excel('packt_output_excel.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9789e48f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " deep_email_recv.py                 \u001b[0m\u001b[01;34m__pycache__\u001b[0m/\r\n",
      " deep_email_send.py                 send_mail.py\r\n",
      "'Extracting Table from PDF.ipynb'   send.py\r\n",
      " hn_news_scraper_no_cred.py         table_from_pdf.csv\r\n",
      " inbox.py                           ted_talk_downloader.py\r\n",
      " packt_output_excel.xlsx            \u001b[01;34mvenv\u001b[0m/\r\n"
     ]
    }
   ],
   "source": [
    "ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a82872d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "61fa7eab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>KPI</th>\n",
       "      <th>2001</th>\n",
       "      <th>2011</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Literacy Rate (%)</td>\n",
       "      <td>64.8</td>\n",
       "      <td>74.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Male Literacy Rate (%)</td>\n",
       "      <td>75.3</td>\n",
       "      <td>82.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Female Literacy Rate (%)</td>\n",
       "      <td>53.7</td>\n",
       "      <td>65.46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                       KPI  2001   2011\n",
       "0           0         Literacy Rate (%)  64.8  74.04\n",
       "1           1    Male Literacy Rate (%)  75.3  82.14\n",
       "2           2  Female Literacy Rate (%)  53.7  65.46"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.read_csv('table_from_pdf.csv')\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1c60a821",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "86968a2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>KPI</th>\n",
       "      <th>year</th>\n",
       "      <th>percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Literacy Rate (%)</td>\n",
       "      <td>2001</td>\n",
       "      <td>64.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male Literacy Rate (%)</td>\n",
       "      <td>2001</td>\n",
       "      <td>75.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Female Literacy Rate (%)</td>\n",
       "      <td>2001</td>\n",
       "      <td>53.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Literacy Rate (%)</td>\n",
       "      <td>2011</td>\n",
       "      <td>74.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male Literacy Rate (%)</td>\n",
       "      <td>2011</td>\n",
       "      <td>82.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Female Literacy Rate (%)</td>\n",
       "      <td>2011</td>\n",
       "      <td>65.46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        KPI  year percentage\n",
       "0         Literacy Rate (%)  2001       64.8\n",
       "1    Male Literacy Rate (%)  2001       75.3\n",
       "2  Female Literacy Rate (%)  2001       53.7\n",
       "3         Literacy Rate (%)  2011      74.04\n",
       "4    Male Literacy Rate (%)  2011      82.14\n",
       "5  Female Literacy Rate (%)  2011      65.46"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_melted = df.melt('KPI', var_name='year', value_name='percentage')\n",
    "df_melted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2917eaf4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='KPI', ylabel='percentage'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='KPI', y='percentage', hue='year', data=df_melted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9334095",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
